Despite the substantial personal and economic burden of mood disorders, understanding the pathological and molecular features of these disorders remains a considerable challenge in psychiatric research. Dysregulated serotonergic and stress pathways appear to be contributing factors in major depression; however, it is likely that numerous other unidentified risk factors exist. Here we propose to investigate the molecular pathology of major depression, using a combined approach of microarray experiments, bioinformatic analysis and anatomical characterization of results. Our central hypothesis states that the biological liability to major depression is reflected in a persistent molecular pathology that is detectable in the postmortem human brain and that affects a cortico-limbic network, whose dysfunction might specifically cause, or at least correlate with, the affective component of depression. Hence, based on microanatomical and functional studies, we will concentrate on two densely interconnected brain areas within this cortical- limbic network of mood regulation: i) the amygdala (AMY), as a brain region that is crucial to the integration and expression of emotions, and ii) the anterior cingulate cortex (ACC), as depression-related functional and morphological changes have been consistently reported in this brain area. As microanatomical studies suggest a glial depression-related pathology in these two brain areas, we will apply novel analytical approaches to separately assess the contribution of altered glial or neuronal functions within the gray matter in correlation with major depression. Together, results from this research proposal could reveal either a general pathway that is common among all depressed subjects and/or specific pathways that may differ as a function of sex and family history of major depression, two factors that are associated with different phenotypic features of depression. The characterization of patterns of nuclei (AMY) and laminar (ACC) changes for selected genes will provide anatomical information to generate network-based hypotheses on the molecular pathology of depression.